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At least 163 records · Page 9

UAV Research at NASA Langley: Towards Safe, Reliable, and Autonomous Operations

Unmanned Aerial Vehicles (UAV) are fundamental components in several aspects of research at NASA Langley, such as flight dynamics, mission-driven airframe design, airspace integration demonstrations, atmospheric science projects, and more. In particular, NASA Langley Research Center (Langley) is using UAVs to develop and demonstrate innovative capabilities that meet the autonomy and robotics challenges that are anticipated in science, space exploration, and aeronautics. These capabilities will enable new NASA missions such as asteroid rendezvous and retrieval (ARRM), Mars exploration, in-situ resource utilization (ISRU), pollution measurements in historically inaccessible areas, and the integration of UAVs into our everyday lives all missions of increasing complexity, distance, pace, and/or accessibility. Building on decades of NASA experience and success in the design, fabrication, and integration of robust and reliable automated systems for space and aeronautics, Langley Autonomy Incubator seeks to bridge the gap between automation and autonomy by enabling safe autonomous operations via onboard sensing and perception systems in both data-rich and data-deprived environments. The Autonomy Incubator is focused on the challenge of mobility and manipulation in dynamic and unstructured environments by integrating technologies such as computer vision, visual odometry, real-time mapping, path planning, object detection and avoidance, object classification, adaptive control, sensor fusion, machine learning, and natural human-machine teaming. These technologies are implemented in an architectural framework developed in-house for easy integration and interoperability of cutting-edge hardware and software.

Davila, Carlos G.↗

Dude Where's My Stars: A Novel Topologically Justified Approach to Star Tracking

In this paper, we consider two novel approaches to celestial navigation for spacecraft. Determining attitude without any prior knowledge using star tracking is known to be a difficult task, particularly given the computational complexity and the many potential sources of misinformation. We consider localization by optimizing matching parameters without explicit star identification in a computationally tractable manner. This is achieved using the mathematical tools of topological data analysis (TDA) and cellular sheaves to study the geometry and distribution of cataloged stars. A framework is gained that enhances the statistical approach to noise handling and false star detection, and heterogeneous sensor fusion. Finally, we discuss confidence bounds and minimum information requirements for successful operation.

Sheaf theory↗

Vision-Based Precision Approach and Landing for Advanced Air Mobility

Advanced Air Mobility (AAM) aircraft require perception systems for precision approach and landing systems (PALS) in urban, suburban, rural, and regional environments. The current state-of-the-art methods approved for automated approach and landing will be difficult to utilize in support of AAM operational concepts. However, there are technology and systems from other applications and lower-TRL research that use vision, IR, radar, and GPS methods to provide baseline perception and sensing requirements for AAM aircraft approach and landing. This paper focuses on vision-based PAL to demonstrate a closed-loop baseline controller while adhering to the Federal Aviation Administration requirements and regulations. The coplanar algorithm determines pose estimation, which feeds into an Extended Kalman filter. Combining IMU with vision creates a sensor fusion navigation solution for GPS-denied environments. The state estimate leads to glideslope and localizer error computations, which will be pertinent for designing and deriving guidance laws and control laws for AAM PALS. The IMU and vision navigation solution provides promising simulation results for AAM PALS, and higher fidelity simulations will include computer graphics rendering and feature correspondence.

Evan Kawamura↗

Overview of NASA's Detailed Investigation into the MEDLI2 Flight Data

- MEDLI2 “Deep Dive” - Why a focused investigation? - Deeper understanding of the MEDLI2 dataset, including TPS in-depth temperatures, backshell pressure, and radiative heat flux - Validate new tools for future Mars missions including Mars Sample Return and Humans-to-Mars - MEDLI2 is considerably more complex than MEDLI - Fully leverage the significant investment in the MEDLI2 instrumentation suite - Core research areas: - PICA-NuSil (PICA-N) testing, model development, and validation - Detailed aeroheating investigations - Sensor fusion of collocated measurements - High-fidelity analyses and uncertainty quantification - Trajectory and aerodynamics investigations

Tom West↗

Local Scale (3-M) Soil Moisture Mapping Using SMAP and Planet Superdove

A capability for mapping meter-level resolution soil moisture with frequent temporal sampling over large regions is essential for quantifying local-scale environmental heterogeneity and eco-hydrologic behavior. However, available surface soil moisture (SSM) products generally involve much coarser grain sizes ranging from 30 m to several 10s of kilometers. Hence a new method is proposed to estimate 3-m resolution SSM using a combination of multi-sensor fusion, machine- learning (ML) and Cumulative Distribution Function (CDF) matching approaches. This method established favorable SSM correspondence between 3-m pixels and overlying 9-km grid cells from overlapping Planet SuperDove (PSD) observations and NASA Soil Moisture Active-Passive (SMAP) mission products. The resulting 3-m SSM predictions showed improved accuracy by reducing ab- solute bias and RMSE by ~0.01 cm3/cm3 over the original SMAP data in relation to in-situ soil moisture measurements for the Australian Yanco region, while preserving the high sampling frequency (1-3 day global revisit) and sensitivity to surface wetness (R 0.865) from SMAP. Heterogeneous soil moisture distributions varying with vegetation biomass gradients and irrigation regimes were generally captured within a selected study area. Further algorithm refinement and implementation for regional applications will allow for improvement in water resources management, precision agriculture, and disaster forecasts and responses.

soil moisture↗

Some Aeronautical Communications Experiments

Classically there has existed an asymmetry between the computing and communicating sides of aerospace systems. Over the past few decades, this asymmetry has shifted to favoring communication link technologies, meaning that advancements in available central processing units (CPUs), storage devices, and internal data buses have stagnated. Indeed, the increased emphasis placed on refining subsystem performance such as with antenna bandwidth in phased arrays, amplifier power efficiency, software defined radio (SDR) flexibility and encoding for data compression and error correction has given rise to successful debuts of multi-gigabit-per-second data return from long space-link distances. These accomplishments are easily quantifiable through link budgets and illustrate what is possible, but also reveal the deficiencies in overall communications capabilities. In particular, the ever-accelerating presence of aerospace vehicles gives rise to newer and larger classes of challenges to address the needs of 21st century systems. Furthermore remote sensing and imaging capabilities have far outpaced our ability to transmit their products to the ground, so we are increasingly dependent on pre-processing and downselection to contend with the communications bottleneck. No longer may we depend upon the constrained logistics in delivering end-to-end data delivery through manual reconfigurations, static event scheduling and execution on a per-vehicle basis, for these methods do not scale and therefore must give way to dynamic, networked approaches with an overall systems view in mind. Emerging mission requirements exhibit a trend toward multiple smaller-scale vehicles working together to perform dissimilar observations. Such operations necessitate sensor fusion across a constellation, and where data processing may be distributed throughout a fairly disconnected network whose topology changes over time in non-deterministic manners. Individual communications link performance is still very relevant to deploying an effective communications system, but now must be embedded within a greater architecture of capability to optimally utilize the bandwidth available from each link to generate an ultimate end-to-end quality of service. The deleterious effects of timing uncertainty across the arrangement presents a challenge to measurement synchronization and delivery, so a successful deployed system needs to be tolerant to the delays inherent in time-of-light between elements and digital processing latencies existing at each node. In this presentation we share the flight test results from a high performance Gbps laser communications terminal evaluated with a suite of store and forward capabilities called High-rate Delay Tolerant Networking (HDTN). The communications payload is operated over Lake Erie across a range of configurations including several convergence layers, and is evaluated to determine recovery time after link disruptions, information loss, efficiency and speed. The effectiveness of utilizing a flying laboratory to increase the Technology Readiness Level (TRL) of an integrated system in relevant environments is discussed, as well as the value of conducting aeronautics experiments to retire risk for technology infusion into space missions. Upcoming flight campaigns will be presented, including opportunities to demonstrate secure command and control, data intensive hyperspectral imaging, quantum link characterization, 4k High Definition (HD) video streaming and internetworked space-ground-aero relay operations. These experiments will pave the way for future missions which will depend upon interoperability across disparate government and privately owned networks, involve contention with uncertain and dynamic timing, and require agility to autonomously configure optimal parameters across networks of ever-increasing size and complexity to ensure data delivery. https://www1.grc.nasa.gov/space/scan/acs/tech-studies/dtn/

Daniel Raible↗

PACE Technical Report Series, Volume 12: The PACE Level 1C data format

NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will make global ocean color and atmospheric measurements to provide extended data records of ocean ecology and global biogeochemistry, along with polarimetric measurements for advanced observations of aerosols, clouds and the ocean. PACE will contain three instruments: the primary Ocean Color Instrument (OCI), and two multi-angle polarimeters (MAPs). The latter instruments are contributed under a ‘Do-No-Harm’ (to the rest of the PACE mission) principle, and the PACE Science Data Processing System (SDPS) is only required to produce Level-1b (geolocated radiances with calibration applied) data without performance requirements. However, there is a strong desire to produce data in a format that merges the disparate spatial resolutions, viewing geometry and sampling nature of the three instruments. Our terminology for this format is Level 1c (L1C). This format will be an input to Level 2 algorithms produced from standalone MAP instrument observations, or from algorithms employing multi-sensor fusion. Creating the L1C format has several components. This includes choice of projection method, the means by which multi-angle views are properly incorporated into that projection (‘aggregation’) the means to represent wavelength and light polarization state, the selection of data to be included within the L1C file, and the handling of ancillary data either required for L1C file generation or needed in that format for L2 processing.

PACE↗

Traffic Prediction for Uncommunicative Aircraft in Terminal Airspace: Development Framework and Performance Evaluations

This paper presents an air traffic prediction algorithm that takes observations of an aircraft and classifies aircraft type, estimates the aircraft's intent to and method of joining an airport traffic pattern, and predicts the aircaft's future trajectory. To develop algorithms that enable autonomous aircraft to safely insert into un-towered traffic patterns, several challenges need to be addressed. These challenges range from traffic detection to sensor fusion to own-ship trajectory replanning. Critical to a trajectory replanning algorithm is information regarding the future behavior of all traffic aircraft in the operational environment. The presented traffic prediction algorithm generates this information using regular measurements of traffic aircraft position and velocity to classify the aircraft by speed-class, estimate how the aircraft will approach the runway, and construct a predicted trajectory to the runway including future positions and velocities at specific times. The predictions of the presented algorithm are the necessary inputs for any downstream traffic pattern sequencing and own-ship trajectory planning routines. The presented algorithm is benchmarked using approximately 300 randomized traffic trajectories, spanning four vehicle weight classes and eight traffic entry types. While the algorithm can process multiple traffic vehicles in the terminal area, there is no prediction of traffic-on-traffic interaction. Each traffic vehicle is processed separately.

John D McMinn↗

Distributed Sensing Node Configuration

A vision and radar sensor integration strategy for surveillance of Advance Air Mobility Mission concept of airspace is contained in the CAD drawing in this publication developed as part of NASA's Transformational Tools and Technology project. High-density airspace operations with multiple aircraft type and potentially noncooperative aircraft require distributed sensor detection and tracking systems to monitor airspace for safe, autonomous flight operations for advanced air mobility, urban air mobility, and high density small uncrewed air systems (SUAS) flight concepts. Contained herein are drawings that are available in the stp and creo file formats of the distributed sensing nodes. Additional dovetail integration is included to enable rapid changing of embedded system. A day in the life node deployment is included in the 2023_last_flight_day.mp4. Please cite this publication if you use these drawings. This sensor configuration methodology is presented in detail in the publication reference below. Chester Dolph, Thomas Lombaerts, Corey A. Ippolito, Vahram Stepanyan, Evan Kawamura, Keerthana Kannan, George Szatkowski, Todd Ferante, Christopher Morris, Federica Vitiello, Flavia Causa, Roberto Opromolla and Giancarmine Fasano. "Distributed Sensor Fusion of Ground and Air Nodes using Vision and Radar Modalities for Tracking Multirotor Small Uncrewed Air Systems and Birds," AIAA 2024-1781. AIAA SCITECH 2024 Forum. January 2024.

Chester V Dolph↗

Sensor-knowledge-command fusion paradigm for man/machine systems

Sensing-knowledge-command (SKC) fusion is presented as a fundamental paradigm of implementing cooperative control for an advanced man-machine system. SKC fusion operates on the 'SKC fusion network,' which represents the connection between sensor data to commands through knowledge. Sensing, knowledge, and command of a human and a machine are tapped into the network to provide inputs, or stimuli, to the network. Such stimuli automatically invoke an SKC fusion process and generate a fused output for cooperative control. Once invoked by stimuli, the SKC fusion process forces the network to converge to a new equilibrium state through the network dynamics composed of data fusion, feature transformation, and constraint propagation. The SKC fusion process thus integrates redundant information, maintains network consistency, identifies faulty data and concepts, and specifies those concepts to be strengthened through sensor planning.

Lee, Sukhan↗

Adaptive Multi-Sensor Localization Information Fusion for Autonomous Urban Air Mobility Operations

An adaptive method is developed to iteratively fuse the information provided by multiple sensors to enable autonomous urban air mobility type operations. First, noisy and bias corrupted IMU readings are processed as soon as they arrive using kinematic equations represented in the vehicle's body frame. To correct the systems drift resulting from the integration, an information content measure is introduced to decide on the environment. For the cluttered environment the information provided by environmental sensors is counted as reliable and the drift correction as accurate. For the open space, the GPS data is counted as reliable, and the drift correction is done based on the GPS readings. The measurement noise effects are minimize using Iterated Extended Kalman Filter framework. The algorithm is implemented in the in-house developed FlightDeckz simulation environment using an IMU model, simulated video recorded from a camera mounted on the vehicle (for the purpose of this study, outside scenery was generated with XPlane), which flies in an urban environment, and GPS data generated from the environment's digital map.

Onboard Perception↗

Robotic Vision Would Combine Optics And Microwaves

Conceptual robot-vision system processes data from both microwave and visible-wavelength sensors. Concept, called "fusion of sensors," yields better information on shapes of some objects than obtained from one type of sensor alone. Proposed system fuses data from television images with polarized low-resolution radar-scattering cross sections. Using iterative procedure, generates successive approximations to shape of target by minimizing differences between observed radar returns and radar returns observed if object characterized by computed scattering transfer matrix.

Krishen, Kuma↗

Effective structural impact detection and localization using convolutional neural network and Bayesian information fusion with limited sensors

Due to their unpredictable nature, many impact events (e.g., overheight vehicles striking on bridges) go unnoticed or get reported many hours later. However, they can induce structural failures or hidden damage that accelerates the structure’s long-term degradation. Therefore, prompt impact detection and localization strategies are essential for early warning of impact events and rapid maintenance of structures. Most existing impact detection strategies are developed for aircraft composite panels utilizing high-rate synchronized measurement from densely deployed sensors. Limited efforts have been made for infrastructure or human habitats which generally require large-scale but low-rate measurement. In particular, due to harsh environments (e.g., deep space habitats under meteoroids), structural impact localization must be robust to limited sensors (e.g., sensor damage during impacts) and multi-source errors (e.g., measurement errors). In this study, an effective impact detection and localization strategy is proposed using a limited number of vibration measurements, especially in harsh environments (e.g. in deep space). Convolutional neural networks are trained for each sensor node and are fused using Bayesian theory to improve the accuracy of impact localization. Special considerations are paid to evaluate the effect of both measurement error and modeling error in the analysis. The proposed strategy is illustrated using 1D structure, and further validated in 3D geodesic dome structure numerically. The results demonstrate that it can detect and localize impact events accurately and robustly on structures.

Yuguang Fu↗

Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion

Robust Subspace Recovery (RoSuRe) algorithm was recently introduced as a principled and numerically efficient algorithm that unfolds underlying Unions of Subspaces (UoS) structure, present in the data. The union of Subspaces (UoS) is capable of identifying more complex trends in data sets than simple linear models. In this work, we build on and extend RoSuRe to prospect the structure of different data modalities individually. We propose a novel multi-modal data fusion approach based on group sparsity which we refer to as Robust Group Subspace Recovery (RoGSuRe). Relying on a bi-sparsity pursuit paradigm and non-smooth optimization techniques, the introduced framework learns a new joint representation of the time series from different data modalities, respecting an underlying UoS model. We subsequently integrate the obtained structures to form a unified subspace structure. The proposed approach exploits the structural dependencies between the different modalities data to cluster the associated target objects. The resulting fusion of the unlabeled sensors’ data from experiments on audio and magnetic data has shown that our method is competitive with other state of the art subspace clustering methods. The resulting UoS structure is employed to classify newly observed data points, highlighting the abstraction capacity of the proposed method.

47 OTHER INSTRUMENTATION↗

Real-time Enhancement, Registration, and Fusion for a Multi-Sensor Enhanced Vision System

Over the last few years NASA Langley Research Center (LaRC) has been developing an Enhanced Vision System (EVS) to aid pilots while flying in poor visibility conditions. The EVS captures imagery using two infrared video cameras. The cameras are placed in an enclosure that is mounted and flown forward-looking underneath the NASA LaRC ARIES 757 aircraft. The data streams from the cameras are processed in real-time and displayed on monitors on-board the aircraft. With proper processing the camera system can provide better-than- human-observed imagery particularly during poor visibility conditions. However, to obtain this goal requires several different stages of processing including enhancement, registration, and fusion, and specialized processing hardware for real-time performance. We are using a real-time implementation of the Retinex algorithm for image enhancement, affine transformations for registration, and weighted sums to perform fusion. All of the algorithms are executed on a single TI DM642 digital signal processor (DSP) clocked at 720 MHz. The image processing components were added to the EVS system, tested, and demonstrated during flight tests in August and September of 2005. In this paper we briefly discuss the EVS image processing hardware and algorithms. We then discuss implementation issues and show examples of the results obtained during flight tests. Keywords: enhanced vision system, image enhancement, retinex, digital signal processing, sensor fusion

Hines, Glenn D.↗